Evaluating User Performance, Workload, and Presence of Virtual Reality Questionnaires Using Joystick and Raycasting Selection Techniques
Xingbo Wei, Yue Li · 2023
Understanding users’ subjective feelings is vital for Virtual Reality (VR) research, and questionnaire is one of the common used approaches to obtain subjective feedback. Embedding questionnaires into VR systems has been shown effective in reducing the break in presence (BIP) and systematic bias compared to filling out questionnaire outside VR. However, it is not clear how users perform and perceive workload and presence of VR questionnaires, and there is no clear guideline for choosing appropriate selection techniques. In this paper, we present an experimental study that examined user performance, workload, and presence of VR questionnaires, and compared them to the use of PC. We investigated two commonly-used selection techniques in VR (joystick selection and raycasting selection) and three question types (radio, block, and slider). Our results showed that despite the benefits of in-VR questionnaires, user performance was better and workload was lower outside VR using a PC. Comparing joystick and raycasting, user workload is slightly lower using raycasting selection, whereas joystick better supports precise selections. There is room for optimizing existing VR questionnaire design and developing novel selection techniques for VR questionnaires.